Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis

Chen, Peng-Ting

doi:10.1016/j.techfore.2018.01.033

Cited by 32 publications

(26 citation statements)

References 45 publications

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“…Although digitalizing medical information can benefit organizations in different ways, many medical institutions have encountered challenges in implementing a medical data system [30,31]. Therefore, how to adopt, supervise, and apply digital technology is important for governing medical information digitalization effectively, and resistance elimination has become the key to the successful application and economic value of medical information digitalization [16,32].…”

Section: Medical Information Digitalization Barriersmentioning

confidence: 99%

“…The IRA model is exclusively developed based on its original construct known as the importance-performance analysis (IPA) model [73]. Similar to IPA, the IRA model retains the same concept and structure, although the latter examines the resistance level rather than the performance of the factors [30]. Figure 1 describes the IRA model containing the four quadrants for strategic decision making through innovation resistance (IRI) and innovation importance (III) indices.…”

Section: Importance-resistance Analysis-network Relation Map (Ira-nrm) Analysismentioning

confidence: 99%

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Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization

Tsai

Wang

et al. 2021

Sustainability

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Healthcare industries are facing an enormous flow of medical records due to the progression of information technology and the trend of digital transformation. Thus, medical information digitalization is a huge digital dataset that can be utilized to benefit healthcare systems and patients. While many studies focus on the application of the digitalized medical information in the healthcare field, only a few mentioned its resistance. The theoretical background depicts a comprehensive overview of medical information digitalization and the barriers in previous literature. This study emphasized the interaction of medical information digitalization barriers and applies the importance-resistance analysis model (IRA) to identify the resistant factors overcoming strategy. It also clarifies the pathway to eliminating the innovation resistance and reveals the interaction of medical information digitalization barriers. The acquisition, management, and application of medical information digitalization are the key foundation of medical technology innovation, digital transformation, and the application of artificial intelligence. This work can reduce the limitation of a narrow healthcare context. This study helps healthcare industries to clarify and solve barriers and realizes the innovation and application of medical information digitalization. In the long term, the results provide a basis for the future development direction of medical information digitalization and affect the medical industry.

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Section: Medical Information Digitalization Barriersmentioning

confidence: 99%

Section: Importance-resistance Analysis-network Relation Map (Ira-nrm) Analysismentioning

confidence: 99%

Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization

Tsai

Wang

et al. 2021

Sustainability

Self Cite

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“…Bridges between big data and foresight are not restricted to qualitative approaches. For example, combinations among qualitative and quantitative techniques are used to link medical big data and scenarios when dealing with resource allocation on health, in which content and statistical analysis of surveys among experts’ opinions on medical big data serve as bridges (Chen 2018).…”

Section: How Futures Studies Could Address Ethical Dilemmas Derived Fmentioning

confidence: 99%

How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence

Domínguez

2019

World Futures Review

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Drawing from ethical concerns raised by communities of machine learning developers and considering predictive analytics’ very short-term predictions, several futures studies techniques are examined to offer some insights about possible bridges between machine learning and foresight. This review develops three main sections: (1) a brief explanation of central concepts, such as big data, machine learning, and artificial intelligence, hopefully not too simplistic but readable for larger audiences; (2) a discussion about ethical issues, such as bias, discrimination, and dilemmas in research; and (3) a brief description of how futures studies could address ethical dilemmas derived from different time horizons among machine learning immediate results, forecasting short-term predictions, and foresight long-term scenarios.

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“…To solve such issues, understanding the technicalities of the current scenario in the big data environment within telemedicine is extremely momentous. Generally, database servers at medical centres holding the typical database management system (DBMS) cannot deal with a maximised number of daily basis produced data [15]. Medical sensors measure the vital signs of patients, who are provided with health care services, by processing several sensor data attached to their bodies [16].…”

Section: Introductionmentioning

confidence: 99%

A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method

et al. 2020

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Telemedicine is increasingly used in the modern health care system because it provides health care services to patients amidst distant locations. However, the prioritisation process for patients with multiple chronic diseases (MCDs) over telemedicine is becoming increasingly complex due to diverse and big data generated from multiple disease conditions. To solve such a problem, massive datasets must be collected, and high velocity must be acquired, specifically in real-time processing. This process requires decision-making (DM) regarding the emergency degree of each chronic disease for every patient. Multi-criteria decisionmaking (MCDM) approaches (i.e. direct aggregation, distance measurement and compromise ranking) are the main solutions for dealing with this complex situation. However, each MCDM approach provides a unique rank from those of other methods. By far, the preferred DM approach that can provide an ideal rank better than other approaches has not been established. This study proposes an extension of the technique for reorganising opinion order to interval levels (TROOIL). Such an extension is called Hybrid DM and Voting Method (HDMVM) which is based on different DM approaches (i.e. direct aggregation, distance measurement and compromise ranking). HDMVM is used to prioritise big data of patients with MCDs in real-time through the remote health-monitoring procedure. In this paper, we propose a methodology that is based on three sequential stages. The first stage illustrates how the big data of patients with MCDs can be recognised in the telemedicine environment and identifies the target telemedicine tier in this study. The second stage describes the steps of the proposed HDMVM sequentially. The third stage applies the proposed method by prioritising the case study of big data of patients with MCDs based on the above DM approaches. Moreover, dataset of remote patients with MCDs (n = 500) is adopted, which contains three diseases, namely, chronic heart diseases and high and low blood pressures. The prioritisation results vary among direct aggregation, distance measurement and compromise approaches. The proposed HDMVM effectively provides a uniform and final ranking result for big data of patients with MCDs. A statistical method (i.e. mean) is performed to objectively validate the ranking results. Significant differences between the scores of the groups are identified in the objective validation, signifying identical ranking results. The evaluation of the proposed work with the benchmark study indicates that this study has tackled issues relevant to big data and diversity of MCDM approaches in the prioritisation of patients with MCDs.

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Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis

Cited by 32 publications

References 45 publications

Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization

Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization

How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence

A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method

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